Model Specification

Overview

  • State-level hierarchical generalized additive model (GAM) that models the prevalence of RR-TB positive cases per quarter among incident TB cases between 2014-2019

  • Fit smoothing functions to reduce the noise we were seeing in previous models

  • Models risk of positivity by characteristics of patient and municipality where they reside

    • Note: Between 2014-2019, ~3,300 cases diagnosed outside of patient’s state of residence; ~88,000 cases diagnosed outside patient’s municipality of residence (e.g. cases are not being attributed to municipalities/states in which cases are generated)

  • Separate models for new cases and previously treated cases (e.g. relapse and re-entry)

Set Up

result ~ s(state, bs = "re") + s(time) + s(time, by = state, id = 1) + age_cat + hiv_status + sex + health_unit + bf_cat + urban_cat + has_prison + fhs_cat

  • Random intercept for each state (patient state of residence)

  • A different smooth function for time by state with a shared smoothing parameter

  • Each state-level smoothing parameter varies around a grand smooth function for time to allow for pooling across states

  • Fixed effects for patient-level characteristics:

    • Age
    • HIV status
    • Sex
    • Level of health unit where patient is diagnosed - Based on CNES merge

  • Fixed effects for characteristics of region where patient resides (either municipality or micro-region level):

    • Urbanicity - Percent of the population in urban setting (2010 census)
    • Bolsa Familia coverage - Percent of the population benefiting from BF (BF: SAGICAD, 2018; Denominator: 2010 Census)
    • Presence of prison - Municipality has prison at some point during 2014-2019 period (SISPEN)
    • FHS Coverage - Average number of health teams per 4,000 people between 2014-2019

Specifications

  • Model 1 - 2014-2019

  • Model 2 - 2016-2019

  • Run separately by case type (new vs. previously treated (relapse, re-entry))

Model Output

National-level estimates

State-level estimates

New Cases

A. Municipality-level

B. Microregion-level

Previous Cases

A. Municipality-level

B. Microregion-level

C. Municipality and Micro-region